Edge ML Model Adaptation for Hardware-Constrained Devices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Implementing sophisticated machine learning models on edge devices in aerospace systems is impractical due to hardware constraints, leading to energy-intensive data transmission and long delays in analysis.
Innovation Solution
Adapt machine learning models for target devices using type-specific adaptation methods, including resource mapping and compression techniques, to optimize performance and compatibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are implemented on edge devices, then processing speed and energy efficiency are improved, but hardware constraints make sophisticated models impractical to implement
Solution Approach 1:
The patent transforms machine learning models by changing their structural parameters to suit edge device constraints. This includes converting complex models into simplified versions with reduced computational requirements, adjusted precision levels, and modified architectures that maintain adequate accuracy while fitting within limited hardware resources of edge devices.
Solution Approach 2:
The patent segments sophisticated machine learning models into smaller, more manageable components that can be individually optimized and executed on resource-constrained edge devices. This segmentation allows the system to process complex tasks through multiple simpler steps rather than requiring a single large model, making deployment on edge devices practical.
2Reliability
If data is gathered and forwarded to remote processors for analysis, then sophisticated machine learning models can be executed, but energy consumption and communication requirements increase
Solution Approach 1:
The patent extracts the machine learning processing capability from remote centralized systems and places it directly on edge devices. By taking out the model execution function from the remote processor and embedding it locally on edge devices, the system eliminates the need for continuous data transmission while maintaining sophisticated analysis capabilities at the source.
3Productivity
If data is transmitted to remote systems for analysis, then comprehensive processing can be performed, but communication delays increase
Solution Approach 1:
The patent performs preliminary action by pre-processing and analyzing data locally on edge devices using deployed machine learning models before any potential remote transmission. This allows immediate insights and decisions to be generated at the edge, with only necessary summarized information or anomalies being transmitted remotely, thereby eliminating most communication delays.
Data Source
Figure 1~2
Figure 3~4
Figure 5~6
AI summary
A computer implemented method of adapting a machine learning model for execution on a target device (102) is provided. The method comprises receiving information describing a trained machine learning model (501, 605, 701); determining a type of the machine learning model; receiving an indication (503, 607) of one or more characteristics of the target device; adapting the machine learning model for the target device using an adaptation method (306, 308) that is selected based on the type of the machine learning model; and outputting information (516, 622, 712) describing the adapted machine learning model.